1. Field of the Invention
The present invention relates to a smoke detection method, and more specifically, to a smoke detection method based on video processing.
2. Description of the Prior Art
Generally, the fire accident usually causes economical and ecological damage as well as endangering people's lives. To avoid the fire's disasters, many early fire-detection techniques have been explored and most of them are based on particle sampling, temperature sampling, relative humidity sampling, air transparency testing, in addition to the traditional ultraviolet and infrared fire detectors. However, most of the objects generate smoke before they catch fire and this motivates that the smoke-detection is employed to provide an early alarm for preventing fire accident effectively.
Almost all traditional smoke-detectors either must be set in the proximity of a fire or can't provide the additional information about the process of burning, such as the burning location, size, growing rate, and so on. Hence, they are not always reliable because energy emission of non-fires or byproducts of combustion, which can be yielded in other ways, may be detected by misadventure. This frequently results in false alarms. To provide more reliable information about smoke-detection, the vision-based approach is becoming more and more interesting.
Most of video-based fire-detection techniques are aimed at flame detection for the purpose of giving a fire alarm. In many practical situations, smoke comes earlier than flame in a burning process and therefore, smoke-detection will offer a more early alarm to a possible fire accident than flame-detection does. By noticing that smoke shapes have the property of self-similarity, the fractal encoding technique is introduced in conventional practice to extract the smoke region and is effective especially for using a monochrome video camera. In other prior art, based on determining the smoke edge regions whose wavelet subband energies decrease with time, these regions are then analyzed along with their corresponding background regions with respect to their RGB and chrominance values to achieve real-time detection of smoke.
To avoid being interfered by smoke-aliases, there are still other techniques in the prior art that employ both chromatic recognition and disorder measurement to improve the verification of real smoke with higher effectiveness. Anyway, the disorder measurement on pixel number of smoke-image difference between two continuous frames is not very effective.